Haihong2019ANB propose a bi-directional model, SF-ID (SF-Intent Detection) network, sharing ideas with goo2018slot, with two key differences. First, in addition to the slot-gated mechanism, they add an intent-gated mechanism as well. Second, they use an iterative mechanism between the SF and ID network, meaning that the gate vector from SF is injected into the ID network and vice versa. This mechanism is repeated for an arbitrary number of iteration. Compared to [1]}, the SF-ID network performs better both in SF and IC on ATIS and SNIPS. The work from Li2018ASM is also similar to goo2018slot with two differences. First, they use a self-attention mechanism [2]} to compute \(c^{S}_{i}\) . Secondly, they use a separate network to compute gate vector \(g^{s}\) , but the input of this network is the concatenation of \(c^{S}_{i}\)  and the intent embedding \(v\) , and \(g^s\)  is defined as \(g^{s} = \texttt {tanh}(W^{g}[c^i_{slot}, v^{intent}] + b^s)\) . After that, \(h_i\)  is combined with \(g^s\)  through element-wise multiplication to compute \(y^s_{i}\)  as follows: \(y^{slot}_i=\texttt {softmax}(W^s(h_i \odot g^s) + b^s\) ). They report a +0.5% improvement on SF over Liu2016AttentionBasedRN. A recent work by Zhang2019AJL, further improves the performance of the BERT based model by adding a gate mechanism [3]} to the BERT model. Table REF  compares the performance of the joint models.
<TABLE>